Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations35064
Missing cells292
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory168.0 B

Variable types

DateTime1
Numeric20

Alerts

forecast solar day ahead is highly overall correlated with generation solarHigh correlation
forecast wind onshore day ahead is highly overall correlated with generation wind onshoreHigh correlation
generation biomass is highly overall correlated with generation other and 1 other fieldsHigh correlation
generation fossil brown coal/lignite is highly overall correlated with generation fossil gas and 1 other fieldsHigh correlation
generation fossil gas is highly overall correlated with generation fossil brown coal/lignite and 4 other fieldsHigh correlation
generation fossil hard coal is highly overall correlated with generation fossil brown coal/lignite and 1 other fieldsHigh correlation
generation fossil oil is highly overall correlated with total load actualHigh correlation
generation hydro pumped storage consumption is highly overall correlated with generation fossil gas and 1 other fieldsHigh correlation
generation hydro run-of-river and poundage is highly overall correlated with generation hydro water reservoirHigh correlation
generation hydro water reservoir is highly overall correlated with generation hydro run-of-river and poundage and 1 other fieldsHigh correlation
generation other is highly overall correlated with generation biomassHigh correlation
generation other renewable is highly overall correlated with generation biomass and 1 other fieldsHigh correlation
generation solar is highly overall correlated with forecast solar day aheadHigh correlation
generation waste is highly overall correlated with generation other renewableHigh correlation
generation wind onshore is highly overall correlated with forecast wind onshore day aheadHigh correlation
hour is highly overall correlated with timeHigh correlation
price actual is highly overall correlated with generation fossil gasHigh correlation
time is highly overall correlated with hourHigh correlation
total load actual is highly overall correlated with generation fossil gas and 3 other fieldsHigh correlation
time has 1461 (4.2%) zerosZeros
generation fossil brown coal/lignite has 10517 (30.0%) zerosZeros
generation hydro pumped storage consumption has 12607 (36.0%) zerosZeros
forecast solar day ahead has 539 (1.5%) zerosZeros
hour has 1461 (4.2%) zerosZeros

Reproduction

Analysis started2024-09-11 14:12:27.102772
Analysis finished2024-09-11 14:13:10.561624
Duration43.46 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

date
Date

Distinct1462
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Minimum2014-12-31 00:00:00
Maximum2018-12-31 00:00:00
2024-09-11T16:13:10.650523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:10.853371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

time
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:10.961183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-09-11T16:13:11.055938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 1461
 
4.2%
0 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
14 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

generation biomass
Real number (ℝ)

HIGH CORRELATION 

Distinct423
Distinct (%)1.2%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean383.51354
Minimum0
Maximum592
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:11.162156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile248
Q1333
median367
Q3433
95-th percentile543
Maximum592
Range592
Interquartile range (IQR)100

Descriptive statistics

Standard deviation85.353943
Coefficient of variation (CV)0.22255783
Kurtosis-0.28342726
Mean383.51354
Median Absolute Deviation (MAD)39
Skewness0.42101261
Sum13440232
Variance7285.2956
MonotonicityNot monotonic
2024-09-11T16:13:11.394985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361 321
 
0.9%
362 318
 
0.9%
351 310
 
0.9%
358 305
 
0.9%
359 305
 
0.9%
357 300
 
0.9%
352 296
 
0.8%
367 296
 
0.8%
381 295
 
0.8%
356 294
 
0.8%
Other values (413) 32005
91.3%
ValueCountFrequency (%)
0 4
< 0.1%
101 1
 
< 0.1%
167 1
 
< 0.1%
168 1
 
< 0.1%
173 2
< 0.1%
174 1
 
< 0.1%
175 1
 
< 0.1%
176 4
< 0.1%
177 1
 
< 0.1%
178 4
< 0.1%
ValueCountFrequency (%)
592 2
 
< 0.1%
591 2
 
< 0.1%
590 3
 
< 0.1%
589 1
 
< 0.1%
588 2
 
< 0.1%
587 3
 
< 0.1%
586 11
< 0.1%
585 6
< 0.1%
584 11
< 0.1%
583 10
< 0.1%

generation fossil brown coal/lignite
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct956
Distinct (%)2.7%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean448.05921
Minimum0
Maximum999
Zeros10517
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:11.514603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median509
Q3757
95-th percentile958
Maximum999
Range999
Interquartile range (IQR)757

Descriptive statistics

Standard deviation354.56859
Coefficient of variation (CV)0.79134316
Kurtosis-1.4560854
Mean448.05921
Median Absolute Deviation (MAD)355
Skewness-0.046830974
Sum15702683
Variance125718.89
MonotonicityNot monotonic
2024-09-11T16:13:11.627638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10517
30.0%
663 165
 
0.5%
664 124
 
0.4%
595 108
 
0.3%
657 103
 
0.3%
892 93
 
0.3%
895 91
 
0.3%
894 89
 
0.3%
590 88
 
0.3%
893 88
 
0.3%
Other values (946) 23580
67.2%
ValueCountFrequency (%)
0 10517
30.0%
1 14
 
< 0.1%
2 14
 
< 0.1%
3 7
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 5
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
999 2
 
< 0.1%
997 2
 
< 0.1%
996 3
 
< 0.1%
995 7
< 0.1%
994 2
 
< 0.1%
993 6
 
< 0.1%
992 7
< 0.1%
991 10
< 0.1%
990 17
< 0.1%
989 15
< 0.1%

generation fossil gas
Real number (ℝ)

HIGH CORRELATION 

Distinct8297
Distinct (%)23.7%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5622.7375
Minimum0
Maximum20034
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:11.744360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3348
Q14126
median4969
Q36429
95-th percentile10259.75
Maximum20034
Range20034
Interquartile range (IQR)2303

Descriptive statistics

Standard deviation2201.8305
Coefficient of variation (CV)0.39159404
Kurtosis3.1854332
Mean5622.7375
Median Absolute Deviation (MAD)1007
Skewness1.615447
Sum1.9705446 × 108
Variance4848057.5
MonotonicityNot monotonic
2024-09-11T16:13:11.859246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3993 24
 
0.1%
4180 24
 
0.1%
4227 21
 
0.1%
3856 21
 
0.1%
4124 20
 
0.1%
3881 20
 
0.1%
3969 20
 
0.1%
4302 20
 
0.1%
4493 20
 
0.1%
4140 20
 
0.1%
Other values (8287) 34836
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
1518 1
< 0.1%
1521 1
< 0.1%
1533 1
< 0.1%
1563 1
< 0.1%
1602 1
< 0.1%
1607 1
< 0.1%
1609 1
< 0.1%
1612 1
< 0.1%
1618 1
< 0.1%
ValueCountFrequency (%)
20034 1
< 0.1%
20023 1
< 0.1%
20019 1
< 0.1%
19821 1
< 0.1%
19458 1
< 0.1%
18981 1
< 0.1%
18872 1
< 0.1%
18847 1
< 0.1%
18613 2
< 0.1%
18488 1
< 0.1%

generation fossil hard coal
Real number (ℝ)

HIGH CORRELATION 

Distinct7266
Distinct (%)20.7%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4256.0657
Minimum0
Maximum8359
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:11.981093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1153
Q12527
median4474
Q35838.75
95-th percentile7268
Maximum8359
Range8359
Interquartile range (IQR)3311.75

Descriptive statistics

Standard deviation1961.601
Coefficient of variation (CV)0.46089537
Kurtosis-1.1239628
Mean4256.0657
Median Absolute Deviation (MAD)1607
Skewness-0.073691491
Sum1.4915808 × 108
Variance3847878.5
MonotonicityNot monotonic
2024-09-11T16:13:12.090197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5266 16
 
< 0.1%
6176 15
 
< 0.1%
4747 15
 
< 0.1%
5324 15
 
< 0.1%
4756 15
 
< 0.1%
5333 15
 
< 0.1%
4974 14
 
< 0.1%
5446 14
 
< 0.1%
4909 14
 
< 0.1%
5236 14
 
< 0.1%
Other values (7256) 34899
99.5%
(Missing) 18
 
0.1%
ValueCountFrequency (%)
0 3
< 0.1%
576 1
 
< 0.1%
593 1
 
< 0.1%
594 1
 
< 0.1%
596 2
< 0.1%
598 1
 
< 0.1%
601 1
 
< 0.1%
606 1
 
< 0.1%
609 1
 
< 0.1%
610 2
< 0.1%
ValueCountFrequency (%)
8359 1
< 0.1%
8315 1
< 0.1%
8313 1
< 0.1%
8303 1
< 0.1%
8302 1
< 0.1%
8299 1
< 0.1%
8293 1
< 0.1%
8278 1
< 0.1%
8274 1
< 0.1%
8270 1
< 0.1%

generation fossil oil
Real number (ℝ)

HIGH CORRELATION 

Distinct321
Distinct (%)0.9%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean298.31979
Minimum0
Maximum449
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:12.204334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile213
Q1263
median300
Q3330
95-th percentile396
Maximum449
Range449
Interquartile range (IQR)67

Descriptive statistics

Standard deviation52.520673
Coefficient of variation (CV)0.17605494
Kurtosis0.06691465
Mean298.31979
Median Absolute Deviation (MAD)33
Skewness0.064468802
Sum10454617
Variance2758.421
MonotonicityNot monotonic
2024-09-11T16:13:12.314720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303 335
 
1.0%
309 328
 
0.9%
304 326
 
0.9%
300 323
 
0.9%
308 320
 
0.9%
315 319
 
0.9%
299 319
 
0.9%
317 316
 
0.9%
302 313
 
0.9%
297 312
 
0.9%
Other values (311) 31834
90.8%
ValueCountFrequency (%)
0 3
< 0.1%
44 1
 
< 0.1%
87 1
 
< 0.1%
106 3
< 0.1%
107 2
< 0.1%
112 1
 
< 0.1%
117 2
< 0.1%
118 1
 
< 0.1%
122 1
 
< 0.1%
129 1
 
< 0.1%
ValueCountFrequency (%)
449 1
 
< 0.1%
445 4
< 0.1%
444 1
 
< 0.1%
442 6
< 0.1%
441 8
< 0.1%
440 5
< 0.1%
439 4
< 0.1%
438 9
< 0.1%
437 9
< 0.1%
436 8
< 0.1%

generation hydro pumped storage consumption
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3311
Distinct (%)9.4%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean475.57734
Minimum0
Maximum4523
Zeros12607
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:12.426792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median68
Q3616
95-th percentile2383
Maximum4523
Range4523
Interquartile range (IQR)616

Descriptive statistics

Standard deviation792.40661
Coefficient of variation (CV)1.6661993
Kurtosis4.197127
Mean475.57734
Median Absolute Deviation (MAD)68
Skewness2.1305883
Sum16666608
Variance627908.24
MonotonicityNot monotonic
2024-09-11T16:13:12.537186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12607
36.0%
1 1641
 
4.7%
2 300
 
0.9%
3 184
 
0.5%
54 130
 
0.4%
4 130
 
0.4%
5 128
 
0.4%
55 101
 
0.3%
6 100
 
0.3%
38 83
 
0.2%
Other values (3301) 19641
56.0%
ValueCountFrequency (%)
0 12607
36.0%
1 1641
 
4.7%
2 300
 
0.9%
3 184
 
0.5%
4 130
 
0.4%
5 128
 
0.4%
6 100
 
0.3%
7 74
 
0.2%
8 63
 
0.2%
9 59
 
0.2%
ValueCountFrequency (%)
4523 1
< 0.1%
4516 1
< 0.1%
4504 1
< 0.1%
4498 1
< 0.1%
4469 1
< 0.1%
4444 1
< 0.1%
4438 1
< 0.1%
4421 1
< 0.1%
4360 1
< 0.1%
4358 1
< 0.1%

generation hydro run-of-river and poundage
Real number (ℝ)

HIGH CORRELATION 

Distinct1684
Distinct (%)4.8%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean972.11611
Minimum0
Maximum2000
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:12.653670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile437
Q1637
median906
Q31250
95-th percentile1731
Maximum2000
Range2000
Interquartile range (IQR)613

Descriptive statistics

Standard deviation400.77754
Coefficient of variation (CV)0.41227332
Kurtosis-0.72028878
Mean972.11611
Median Absolute Deviation (MAD)297
Skewness0.5009275
Sum34067809
Variance160622.63
MonotonicityNot monotonic
2024-09-11T16:13:12.770252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 59
 
0.2%
632 58
 
0.2%
615 56
 
0.2%
552 56
 
0.2%
553 56
 
0.2%
560 55
 
0.2%
631 53
 
0.2%
605 53
 
0.2%
690 53
 
0.2%
595 53
 
0.2%
Other values (1674) 34493
98.4%
ValueCountFrequency (%)
0 3
< 0.1%
283 1
 
< 0.1%
285 1
 
< 0.1%
287 1
 
< 0.1%
289 1
 
< 0.1%
291 2
< 0.1%
292 2
< 0.1%
293 4
< 0.1%
295 1
 
< 0.1%
297 1
 
< 0.1%
ValueCountFrequency (%)
2000 1
< 0.1%
1995 1
< 0.1%
1991 1
< 0.1%
1989 1
< 0.1%
1984 1
< 0.1%
1981 2
< 0.1%
1980 1
< 0.1%
1978 1
< 0.1%
1977 1
< 0.1%
1976 1
< 0.1%

generation hydro water reservoir
Real number (ℝ)

HIGH CORRELATION 

Distinct7029
Distinct (%)20.1%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2605.1147
Minimum0
Maximum9728
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:12.894134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile471
Q11077.25
median2164
Q33757
95-th percentile6234.75
Maximum9728
Range9728
Interquartile range (IQR)2679.75

Descriptive statistics

Standard deviation1835.1997
Coefficient of variation (CV)0.70446024
Kurtosis0.17442455
Mean2605.1147
Median Absolute Deviation (MAD)1246
Skewness0.89703637
Sum91298851
Variance3367958.1
MonotonicityNot monotonic
2024-09-11T16:13:13.008076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801 26
 
0.1%
621 22
 
0.1%
1311 21
 
0.1%
551 21
 
0.1%
559 21
 
0.1%
1099 21
 
0.1%
876 20
 
0.1%
752 20
 
0.1%
724 20
 
0.1%
781 20
 
0.1%
Other values (7019) 34834
99.3%
ValueCountFrequency (%)
0 3
< 0.1%
134 1
 
< 0.1%
151 1
 
< 0.1%
155 1
 
< 0.1%
157 1
 
< 0.1%
159 1
 
< 0.1%
161 1
 
< 0.1%
163 2
< 0.1%
169 3
< 0.1%
170 1
 
< 0.1%
ValueCountFrequency (%)
9728 1
< 0.1%
9477 1
< 0.1%
9389 1
< 0.1%
9355 1
< 0.1%
9313 1
< 0.1%
9272 1
< 0.1%
9267 1
< 0.1%
9203 1
< 0.1%
9186 1
< 0.1%
9182 1
< 0.1%

generation nuclear
Real number (ℝ)

Distinct2388
Distinct (%)6.8%
Missing17
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6263.907
Minimum0
Maximum7117
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:13.121924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5017
Q15760
median6566
Q37025
95-th percentile7106
Maximum7117
Range7117
Interquartile range (IQR)1265

Descriptive statistics

Standard deviation839.66796
Coefficient of variation (CV)0.1340486
Kurtosis-0.50106527
Mean6263.907
Median Absolute Deviation (MAD)532
Skewness-0.69168291
Sum2.1953115 × 108
Variance705042.28
MonotonicityNot monotonic
2024-09-11T16:13:13.232918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7102 376
 
1.1%
7103 367
 
1.0%
7104 366
 
1.0%
7101 364
 
1.0%
7098 344
 
1.0%
7097 333
 
0.9%
7099 330
 
0.9%
7096 324
 
0.9%
7107 323
 
0.9%
7105 319
 
0.9%
Other values (2378) 31601
90.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
998 1
 
< 0.1%
3712 1
 
< 0.1%
3722 1
 
< 0.1%
3731 1
 
< 0.1%
3732 2
 
< 0.1%
3733 6
< 0.1%
3734 4
 
< 0.1%
3735 14
< 0.1%
3736 6
< 0.1%
ValueCountFrequency (%)
7117 6
 
< 0.1%
7116 12
 
< 0.1%
7115 30
 
0.1%
7114 46
 
0.1%
7113 103
 
0.3%
7112 155
0.4%
7111 178
0.5%
7110 229
0.7%
7109 258
0.7%
7108 280
0.8%

generation other
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)0.3%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean60.228585
Minimum0
Maximum106
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:13.353072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q153
median57
Q380
95-th percentile90
Maximum106
Range106
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.238381
Coefficient of variation (CV)0.33602617
Kurtosis0.44309684
Mean60.228585
Median Absolute Deviation (MAD)6
Skewness-0.50619579
Sum2110771
Variance409.59206
MonotonicityNot monotonic
2024-09-11T16:13:13.467004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 2288
 
6.5%
56 2271
 
6.5%
55 2183
 
6.2%
58 2093
 
6.0%
54 1775
 
5.1%
59 1599
 
4.6%
53 1415
 
4.0%
52 1227
 
3.5%
60 1060
 
3.0%
87 936
 
2.7%
Other values (93) 18199
51.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
3 1
 
< 0.1%
4 4
 
< 0.1%
5 16
 
< 0.1%
6 57
 
0.2%
7 141
0.4%
8 91
 
0.3%
9 113
 
0.3%
10 152
0.4%
11 308
0.9%
ValueCountFrequency (%)
106 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 3
 
< 0.1%
100 22
 
0.1%
99 35
 
0.1%
98 42
 
0.1%
97 61
0.2%
96 104
0.3%
95 117
0.3%

generation other renewable
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)0.2%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean85.639702
Minimum0
Maximum119
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:13.578006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile63
Q173
median88
Q397
95-th percentile106
Maximum119
Range119
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.077554
Coefficient of variation (CV)0.16438117
Kurtosis-0.82664691
Mean85.639702
Median Absolute Deviation (MAD)11
Skewness-0.21607268
Sum3001329
Variance198.17753
MonotonicityNot monotonic
2024-09-11T16:13:13.693002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 990
 
2.8%
92 981
 
2.8%
99 978
 
2.8%
93 961
 
2.7%
96 954
 
2.7%
95 946
 
2.7%
98 941
 
2.7%
91 915
 
2.6%
90 896
 
2.6%
97 891
 
2.5%
Other values (68) 25593
73.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
4 1
 
< 0.1%
14 1
 
< 0.1%
43 2
 
< 0.1%
45 1
 
< 0.1%
47 3
 
< 0.1%
48 4
 
< 0.1%
49 3
 
< 0.1%
50 12
 
< 0.1%
51 32
0.1%
ValueCountFrequency (%)
119 2
 
< 0.1%
118 3
 
< 0.1%
117 8
 
< 0.1%
116 12
 
< 0.1%
115 31
 
0.1%
114 43
 
0.1%
113 77
 
0.2%
112 120
0.3%
111 157
0.4%
110 215
0.6%

generation solar
Real number (ℝ)

HIGH CORRELATION 

Distinct5331
Distinct (%)15.2%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1432.6659
Minimum0
Maximum5792
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:13.809957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q171
median616
Q32578
95-th percentile4927.75
Maximum5792
Range5792
Interquartile range (IQR)2507

Descriptive statistics

Standard deviation1680.1199
Coefficient of variation (CV)1.1727227
Kurtosis-0.38778007
Mean1432.6659
Median Absolute Deviation (MAD)585
Skewness1.0203875
Sum50209210
Variance2822802.8
MonotonicityNot monotonic
2024-09-11T16:13:13.919247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 258
 
0.7%
20 247
 
0.7%
23 236
 
0.7%
21 225
 
0.6%
27 224
 
0.6%
17 219
 
0.6%
18 209
 
0.6%
30 199
 
0.6%
15 196
 
0.6%
31 196
 
0.6%
Other values (5321) 32837
93.6%
ValueCountFrequency (%)
0 3
 
< 0.1%
2 10
 
< 0.1%
3 30
 
0.1%
4 103
0.3%
5 51
0.1%
6 78
0.2%
7 50
0.1%
8 16
 
< 0.1%
9 69
0.2%
10 76
0.2%
ValueCountFrequency (%)
5792 2
< 0.1%
5781 1
< 0.1%
5779 1
< 0.1%
5775 1
< 0.1%
5767 1
< 0.1%
5744 1
< 0.1%
5742 1
< 0.1%
5736 1
< 0.1%
5714 1
< 0.1%
5684 1
< 0.1%

generation waste
Real number (ℝ)

HIGH CORRELATION 

Distinct262
Distinct (%)0.7%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean269.45213
Minimum0
Maximum357
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:14.156248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile167
Q1240
median279
Q3310
95-th percentile331
Maximum357
Range357
Interquartile range (IQR)70

Descriptive statistics

Standard deviation50.195536
Coefficient of variation (CV)0.18628739
Kurtosis0.14136656
Mean269.45213
Median Absolute Deviation (MAD)33
Skewness-0.84912856
Sum9442950
Variance2519.5918
MonotonicityNot monotonic
2024-09-11T16:13:14.267755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317 405
 
1.2%
312 398
 
1.1%
316 397
 
1.1%
319 391
 
1.1%
314 387
 
1.1%
315 384
 
1.1%
311 369
 
1.1%
313 367
 
1.0%
318 364
 
1.0%
320 358
 
1.0%
Other values (252) 31225
89.1%
ValueCountFrequency (%)
0 3
< 0.1%
39 1
 
< 0.1%
84 1
 
< 0.1%
85 1
 
< 0.1%
86 2
< 0.1%
87 1
 
< 0.1%
88 1
 
< 0.1%
90 3
< 0.1%
92 2
< 0.1%
100 1
 
< 0.1%
ValueCountFrequency (%)
357 1
 
< 0.1%
356 1
 
< 0.1%
355 5
 
< 0.1%
354 5
 
< 0.1%
353 5
 
< 0.1%
351 13
< 0.1%
350 21
0.1%
349 17
< 0.1%
348 24
0.1%
347 29
0.1%

generation wind onshore
Real number (ℝ)

HIGH CORRELATION 

Distinct11465
Distinct (%)32.7%
Missing18
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5464.4798
Minimum0
Maximum17436
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:14.386768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1350
Q12933
median4849
Q37398
95-th percentile11875.75
Maximum17436
Range17436
Interquartile range (IQR)4465

Descriptive statistics

Standard deviation3213.6916
Coefficient of variation (CV)0.58810568
Kurtosis0.057887674
Mean5464.4798
Median Absolute Deviation (MAD)2157
Skewness0.78500755
Sum1.9150816 × 108
Variance10327814
MonotonicityNot monotonic
2024-09-11T16:13:14.500552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3932 15
 
< 0.1%
2845 15
 
< 0.1%
2422 14
 
< 0.1%
2590 14
 
< 0.1%
3488 13
 
< 0.1%
3209 13
 
< 0.1%
3973 12
 
< 0.1%
4171 12
 
< 0.1%
1610 12
 
< 0.1%
5293 12
 
< 0.1%
Other values (11455) 34914
99.6%
(Missing) 18
 
0.1%
ValueCountFrequency (%)
0 3
< 0.1%
234 1
 
< 0.1%
243 1
 
< 0.1%
251 1
 
< 0.1%
255 1
 
< 0.1%
259 1
 
< 0.1%
260 1
 
< 0.1%
265 1
 
< 0.1%
273 1
 
< 0.1%
279 1
 
< 0.1%
ValueCountFrequency (%)
17436 1
< 0.1%
17344 1
< 0.1%
17321 1
< 0.1%
17287 1
< 0.1%
17256 1
< 0.1%
17125 1
< 0.1%
17077 1
< 0.1%
17021 1
< 0.1%
17007 1
< 0.1%
16936 1
< 0.1%

forecast solar day ahead
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5356
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1439.0667
Minimum0
Maximum5836
Zeros539
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:14.617240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q169
median576
Q32636
95-th percentile4891.85
Maximum5836
Range5836
Interquartile range (IQR)2567

Descriptive statistics

Standard deviation1677.7034
Coefficient of variation (CV)1.1658273
Kurtosis-0.44085395
Mean1439.0667
Median Absolute Deviation (MAD)565
Skewness0.99001776
Sum50459436
Variance2814688.5
MonotonicityNot monotonic
2024-09-11T16:13:14.725143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 555
 
1.6%
0 539
 
1.5%
11 508
 
1.4%
1 373
 
1.1%
12 358
 
1.0%
9 348
 
1.0%
2 295
 
0.8%
13 281
 
0.8%
8 238
 
0.7%
15 229
 
0.7%
Other values (5346) 31340
89.4%
ValueCountFrequency (%)
0 539
1.5%
1 373
1.1%
2 295
0.8%
3 223
0.6%
4 209
 
0.6%
5 188
 
0.5%
6 192
 
0.5%
7 195
 
0.6%
8 238
0.7%
9 348
1.0%
ValueCountFrequency (%)
5836 1
< 0.1%
5826 1
< 0.1%
5802 1
< 0.1%
5801 1
< 0.1%
5796 1
< 0.1%
5791 1
< 0.1%
5765 1
< 0.1%
5754 1
< 0.1%
5752 1
< 0.1%
5750 1
< 0.1%

forecast wind onshore day ahead
Real number (ℝ)

HIGH CORRELATION 

Distinct11332
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5471.2167
Minimum237
Maximum17430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:14.841381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum237
5-th percentile1409.15
Q12979
median4855
Q37353
95-th percentile11811
Maximum17430
Range17193
Interquartile range (IQR)4374

Descriptive statistics

Standard deviation3176.3129
Coefficient of variation (CV)0.58054964
Kurtosis0.10195752
Mean5471.2167
Median Absolute Deviation (MAD)2101.5
Skewness0.80041942
Sum1.9184274 × 108
Variance10088963
MonotonicityNot monotonic
2024-09-11T16:13:14.954252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2802 14
 
< 0.1%
3488 13
 
< 0.1%
3575 13
 
< 0.1%
3416 12
 
< 0.1%
3196 12
 
< 0.1%
2623 12
 
< 0.1%
2584 12
 
< 0.1%
2898 12
 
< 0.1%
3594 12
 
< 0.1%
4959 12
 
< 0.1%
Other values (11322) 34940
99.6%
ValueCountFrequency (%)
237 1
< 0.1%
250 1
< 0.1%
254 1
< 0.1%
265 1
< 0.1%
277 1
< 0.1%
278 2
< 0.1%
291 1
< 0.1%
296 1
< 0.1%
299 1
< 0.1%
300 1
< 0.1%
ValueCountFrequency (%)
17430 1
< 0.1%
17385 1
< 0.1%
17280 1
< 0.1%
17229 1
< 0.1%
17211 1
< 0.1%
17197 1
< 0.1%
17071 1
< 0.1%
16996 1
< 0.1%
16916 1
< 0.1%
16873 2
< 0.1%

total load actual
Real number (ℝ)

HIGH CORRELATION 

Distinct15127
Distinct (%)43.2%
Missing36
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean28696.94
Minimum18041
Maximum41015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:15.067725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18041
5-th percentile21655
Q124807.75
median28901
Q332192
95-th percentile36081.65
Maximum41015
Range22974
Interquartile range (IQR)7384.25

Descriptive statistics

Standard deviation4574.988
Coefficient of variation (CV)0.15942424
Kurtosis-0.91509424
Mean28696.94
Median Absolute Deviation (MAD)3718.5
Skewness0.061900904
Sum1.0051964 × 109
Variance20930515
MonotonicityNot monotonic
2024-09-11T16:13:15.181474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23665 12
 
< 0.1%
30960 10
 
< 0.1%
33176 10
 
< 0.1%
32023 10
 
< 0.1%
25043 9
 
< 0.1%
31653 9
 
< 0.1%
23000 9
 
< 0.1%
29537 9
 
< 0.1%
31385 9
 
< 0.1%
30948 9
 
< 0.1%
Other values (15117) 34932
99.6%
(Missing) 36
 
0.1%
ValueCountFrequency (%)
18041 1
< 0.1%
18054 1
< 0.1%
18178 1
< 0.1%
18179 1
< 0.1%
18234 1
< 0.1%
18253 1
< 0.1%
18279 1
< 0.1%
18310 1
< 0.1%
18352 1
< 0.1%
18380 1
< 0.1%
ValueCountFrequency (%)
41015 1
< 0.1%
40939 1
< 0.1%
40916 1
< 0.1%
40900 1
< 0.1%
40693 1
< 0.1%
40620 1
< 0.1%
40324 1
< 0.1%
40306 1
< 0.1%
40295 1
< 0.1%
40241 1
< 0.1%

price actual
Real number (ℝ)

HIGH CORRELATION 

Distinct6653
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.884023
Minimum9.33
Maximum116.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:15.296015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.33
5-th percentile32.56
Q149.3475
median58.02
Q368.01
95-th percentile79.17
Maximum116.8
Range107.47
Interquartile range (IQR)18.6625

Descriptive statistics

Standard deviation14.204083
Coefficient of variation (CV)0.24538867
Kurtosis0.46956114
Mean57.884023
Median Absolute Deviation (MAD)9.35
Skewness-0.32374929
Sum2029645.4
Variance201.75598
MonotonicityNot monotonic
2024-09-11T16:13:15.412422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.85 24
 
0.1%
55.91 22
 
0.1%
56.71 22
 
0.1%
52.35 21
 
0.1%
51.33 21
 
0.1%
58.75 21
 
0.1%
57.09 21
 
0.1%
57.64 21
 
0.1%
55.68 20
 
0.1%
55.23 20
 
0.1%
Other values (6643) 34851
99.4%
ValueCountFrequency (%)
9.33 1
< 0.1%
9.38 1
< 0.1%
9.69 1
< 0.1%
9.85 1
< 0.1%
10.07 1
< 0.1%
10.18 1
< 0.1%
10.66 1
< 0.1%
10.77 1
< 0.1%
11.04 1
< 0.1%
11.23 1
< 0.1%
ValueCountFrequency (%)
116.8 1
< 0.1%
112.81 1
< 0.1%
112.71 1
< 0.1%
111.1 1
< 0.1%
110.86 1
< 0.1%
110.63 1
< 0.1%
110.35 1
< 0.1%
110.19 1
< 0.1%
110.16 2
< 0.1%
109.85 1
< 0.1%

hour
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-09-11T16:13:15.511495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-09-11T16:13:15.604345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 1461
 
4.2%
0 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
14 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

Interactions

2024-09-11T16:13:07.799792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:28.478274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:30.499298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:32.603256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:34.616853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.772968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.676668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.698359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.787358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.909434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:47.091129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:49.107642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:51.091416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-09-11T16:12:56.281870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:58.331885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:00.696256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:02.834272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:04.854883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:06.858714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:08.814636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:29.536850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:31.576186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:33.678097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:35.822167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:37.830354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:39.826161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:41.904654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:43.934678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.200237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.204340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.149667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:52.317955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.272030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:56.380011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:58.431756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:00.794553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:02.933454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:04.946661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:06.949376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:08.906620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:29.632025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:31.675904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:33.777675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:35.925696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:37.924873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:39.922559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.002534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.046440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.299809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.307653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.246657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:52.414605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.372719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:56.478991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:58.537940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:00.902153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.034942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.041846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.045169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:09.000322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:29.733861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:31.775475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:33.884350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.042442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.019260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.018747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.099320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.167756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.396436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.406082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.342855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:52.512948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.473221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:56.583028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:58.761374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:01.006408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.137052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.135412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.139546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:09.095996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:29.835046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:31.876472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:34.006964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.164629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.118080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.120997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.199232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.291441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.498695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.510233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.441445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:52.613638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.573841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:56.687005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:58.864775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:01.119195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.242699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.233609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.238462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:09.194859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:29.939022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:31.978605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:34.122022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.269535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.216978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.220939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.304893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.403717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.599223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.617746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.541158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:52.720010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.677080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:56.791238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:58.978222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:01.234636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.416968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.333094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.337026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:09.286485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:30.116175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:32.076726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:34.231881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.375521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.311760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.319886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.403708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.507190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.703445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.719243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.705911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:52.827090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.776818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:56.898730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:59.091482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:01.334245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.518029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.429387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.432721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:09.383483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:30.222838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:32.179898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:34.335840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.482961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.409943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.423408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.509965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.615848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.805120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.827422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.805567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:52.929647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.885608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:57.005613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:59.217221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:01.460480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.621599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.528791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.533917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:09.470483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:30.312500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:32.270177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:34.429765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.578942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.498731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.516353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.601581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.713610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.899097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:48.920030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.897067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:53.020690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:54.977454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:57.100411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:59.333747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:01.584611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.715458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.616279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.622941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:09.560104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:30.405531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:32.501858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:34.525442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:36.678354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:38.590918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:40.607981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:42.698135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:44.813018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:46.995410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:49.016301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:50.999082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:53.118132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:55.080261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:57.200027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:12:59.434991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:01.695731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:03.813854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:05.828345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:13:07.712537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-09-11T16:13:15.707750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
forecast solar day aheadforecast wind onshore day aheadgeneration biomassgeneration fossil brown coal/lignitegeneration fossil gasgeneration fossil hard coalgeneration fossil oilgeneration hydro pumped storage consumptiongeneration hydro run-of-river and poundagegeneration hydro water reservoirgeneration nucleargeneration othergeneration other renewablegeneration solargeneration wastegeneration wind onshorehourprice actualtimetotal load actual
forecast solar day ahead1.000-0.183-0.0060.0420.1300.0450.124-0.3400.0720.203-0.077-0.0070.0480.9780.004-0.1810.1450.1440.1450.445
forecast wind onshore day ahead-0.1831.000-0.072-0.427-0.471-0.428-0.0500.3480.217-0.0500.1210.015-0.120-0.178-0.1320.9940.084-0.2160.0840.013
generation biomass-0.006-0.0721.0000.2370.0290.4010.404-0.028-0.242-0.028-0.0230.551-0.5280.018-0.330-0.0690.0210.1220.0210.093
generation fossil brown coal/lignite0.042-0.4270.2371.0000.5680.7640.312-0.353-0.498-0.171-0.0700.1060.0830.0490.215-0.4250.0570.3670.0570.290
generation fossil gas0.130-0.4710.0290.5681.0000.6260.368-0.599-0.2720.090-0.163-0.0210.3560.1190.292-0.4730.1930.5160.1930.552
generation fossil hard coal0.045-0.4280.4010.7640.6261.0000.418-0.420-0.458-0.105-0.0840.233-0.0220.0560.109-0.4260.1020.4600.1020.392
generation fossil oil0.124-0.0500.4040.3120.3680.4181.000-0.338-0.0740.177-0.0150.344-0.0870.133-0.184-0.0470.1570.2680.1570.516
generation hydro pumped storage consumption-0.3400.348-0.028-0.353-0.599-0.420-0.3381.0000.035-0.3870.030-0.005-0.259-0.322-0.1580.353-0.346-0.447-0.346-0.666
generation hydro run-of-river and poundage0.0720.217-0.242-0.498-0.272-0.458-0.0740.0351.0000.6400.041-0.0530.0520.055-0.2660.2130.072-0.0870.0720.144
generation hydro water reservoir0.203-0.050-0.028-0.1710.090-0.1050.177-0.3870.6401.0000.0250.137-0.0470.189-0.269-0.0570.2580.1590.2580.521
generation nuclear-0.0770.121-0.023-0.070-0.163-0.084-0.0150.0300.0410.0251.0000.038-0.058-0.0660.0420.123-0.010-0.098-0.0100.076
generation other-0.0070.0150.5510.106-0.0210.2330.344-0.005-0.0530.1370.0381.000-0.3900.003-0.3650.0180.0150.1230.0150.147
generation other renewable0.048-0.120-0.5280.0830.356-0.022-0.087-0.2590.052-0.047-0.058-0.3901.0000.0240.597-0.1230.1020.2490.1020.181
generation solar0.978-0.1780.0180.0490.1190.0560.133-0.3220.0550.189-0.0660.0030.0241.000-0.018-0.1750.1500.1330.1500.437
generation waste0.004-0.132-0.3300.2150.2920.109-0.184-0.158-0.266-0.2690.042-0.3650.597-0.0181.000-0.1300.0090.1260.0090.057
generation wind onshore-0.1810.994-0.069-0.425-0.473-0.426-0.0470.3530.213-0.0570.1230.018-0.123-0.175-0.1301.0000.085-0.2190.0850.013
hour0.1450.0840.0210.0570.1930.1020.157-0.3460.0720.258-0.0100.0150.1020.1500.0090.0851.0000.2471.0000.397
price actual0.144-0.2160.1220.3670.5160.4600.268-0.447-0.0870.159-0.0980.1230.2490.1330.126-0.2190.2471.0000.2470.436
time0.1450.0840.0210.0570.1930.1020.157-0.3460.0720.258-0.0100.0150.1020.1500.0090.0851.0000.2471.0000.397
total load actual0.4450.0130.0930.2900.5520.3920.516-0.6660.1440.5210.0760.1470.1810.4370.0570.0130.3970.4360.3971.000

Missing values

2024-09-11T16:13:09.703790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-11T16:13:10.012407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-11T16:13:10.344606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datetimegeneration biomassgeneration fossil brown coal/lignitegeneration fossil gasgeneration fossil hard coalgeneration fossil oilgeneration hydro pumped storage consumptiongeneration hydro run-of-river and poundagegeneration hydro water reservoirgeneration nucleargeneration othergeneration other renewablegeneration solargeneration wastegeneration wind onshoreforecast solar day aheadforecast wind onshore day aheadtotal load actualprice actualhour
02014-12-3123447.0329.04844.04821.0162.0863.01051.01899.07096.043.073.049.0196.06378.017.06436.025385.065.4123
12015-01-010449.0328.05196.04755.0158.0920.01009.01658.07096.043.071.050.0195.05890.016.05856.024382.064.920
22015-01-011448.0323.04857.04581.0157.01164.0973.01371.07099.043.073.050.0196.05461.08.05454.022734.064.481
32015-01-012438.0254.04314.04131.0160.01503.0949.0779.07098.043.075.050.0191.05238.02.05151.021286.059.322
42015-01-013428.0187.04130.03840.0156.01826.0953.0720.07097.043.074.042.0189.04935.09.04861.020264.056.043
52015-01-014410.0178.04038.03590.0156.02109.0952.0743.07098.043.074.034.0188.04618.04.04617.019905.053.634
62015-01-015401.0172.04040.03368.0158.02108.0961.0848.07098.043.074.034.0186.04397.03.04276.020010.051.735
72015-01-016408.0172.04030.03208.0160.02031.0983.01012.07099.043.072.035.0189.03992.012.03994.020377.051.436
82015-01-017413.0177.04052.03335.0161.02119.01001.01015.07098.043.073.054.0198.03629.039.03602.020094.048.987
92015-01-018419.0177.04137.03437.0163.02170.01041.01357.07097.043.074.0743.0198.03073.0784.03212.020637.054.208
datetimegeneration biomassgeneration fossil brown coal/lignitegeneration fossil gasgeneration fossil hard coalgeneration fossil oilgeneration hydro pumped storage consumptiongeneration hydro run-of-river and poundagegeneration hydro water reservoirgeneration nucleargeneration othergeneration other renewablegeneration solargeneration wastegeneration wind onshoreforecast solar day aheadforecast wind onshore day aheadtotal load actualprice actualhour
350542018-12-3113297.00.06062.02698.0212.0246.01053.02060.06070.060.094.03841.0294.02289.03997.02084.027988.071.9513
350552018-12-3114295.00.06128.02501.0178.0339.01052.01889.06072.060.092.03693.0287.02233.03763.02101.027009.070.8514
350562018-12-3115300.00.06379.02503.0178.0323.01048.02017.06072.061.096.02944.0294.02267.02841.02283.026449.071.3615
350572018-12-3116298.00.06892.02583.0177.0124.01088.02664.06072.061.095.01320.0296.02550.01318.02607.026738.075.1316
350582018-12-3117293.00.07593.02604.0178.01.01131.04005.06072.061.092.0266.0289.02952.0300.03028.029592.077.6117
350592018-12-3118297.00.07634.02628.0178.01.01135.04836.06073.063.095.085.0277.03113.096.03253.030653.077.0218
350602018-12-3119296.00.07241.02566.0174.01.01172.03931.06074.062.095.033.0280.03288.051.03353.029735.076.1619
350612018-12-3120292.00.07025.02422.0168.050.01148.02831.06076.061.094.031.0286.03503.036.03404.028071.074.3020
350622018-12-3121293.00.06562.02293.0163.0108.01128.02068.06075.061.093.031.0287.03586.029.03273.025801.069.8921
350632018-12-3122290.00.06926.02166.0163.0108.01069.01686.06075.061.092.031.0287.03651.026.03117.024455.069.8822